A computational biology group interested in developing statistical computational methods to understand regulatory networks driving cellular functions. The lab works to identify networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and construct predictive models from these networks.
Machine learning is a form of artificial intelligence by which algorithms are “trained” to analyze new information using existing data. Researchers are using it to identify individuals with a genetic condition known as fragile X premutation.
In a paper in Cell Systems, Sushmita Roy and colleagues develop a probabilistic graphical model-based method, multi-species regulatory network learning that uses a phylogenetic framework to infer regulatory networks in multiple species simultaneously.
WID researchers Stephen Wright and Robert Nowak are part of a UW2020: WARF Discovery Initiative project to create machine learning tools that dramatically reduce the time and cost associated with screening compounds for therapeutic relevance.
UW program powers New Yorker contest featuring Rob Nowak and NEXT software
Systems Biology researcher Sushmita Roy is leading an effort putting computational methods to work characterizing the gene regulatory networks responsible for cell differentiation.
WID Optimization researchers have partnered with faculty across campus to work on ways to use computers to make better use of human brain power.
Systems Biology researchers Deborah Chasman and Sushmita Roy are using machine learning to identify virus and pathogenicity-specific regulatory networks which may guide the design of effective therapeutics for infectious diseases. The work is described in a recent paper in PLOS Computational Biology.
The big data phenomenon also begun to take hold in and around the city, from institutions like state and city government to the health care industry.
Discovery fellows Rebecca Willett and Rob Nowak are creating algorithms to make sense of big data and help machines learn.
The magazine is using crowdsourcing algorithms developed by WID researchers to find the funniest cartoon captions
The New Yorker is using a machine learning system developed by WID Optimization researchers to sort through captions for their weekly cartoon caption contest.
With the aid of entrepreneur Joe Sheahan ’04, Discovery Fellow Rob Nowak, ’90, MS’91, PhD’95 and Kevin Jamieson, PhD ’15 poured their thought experiment into the iPhone marketplace.
David Page tackles relational databases and algorithms to predict and improve patient health.
Discovery Fellow Rebecca Willett co-organizes first ever UW-Madison Neuroimaging, Computational Neuroscience, and Neuroengineering Workshop